Nutritional Quality of Tomato (Lycopersicon esculentum Mill) as Influenced by Mulching, Nitrogen and Irrigation Interval
Bibliographic record
Abstract
An experiment was conducted in 2006/07 dry season to evaluate the effect of mulching, nitrogen and irrigationinterval on the nutritional quality of Tomato (Lycopersicon esculentum Mill) at Shika, Nigeria. Treatmentsconsisted of three mulching (no mulch, rice-straw mulch and black polythene mulch) four nitrogen rates (0, 45,90 and 135kgN ha-1) and three irrigation intervals (5, 10 and 15 days). Mulching significantly increased the drymatter, protein and carbohydrate contents in fruits, but decreased the crude fiber content. In most cases rice-strawmulch appeared a better mulching material. N rate of 45kg ha-1 had more dry matter content over control, buthigher values for protein and carbohydrate contents were with 90kg ha-1. The 135kgN ha-1 rate depressedcarbohydrate content. Irrigation interval of 10 days recorded more dry matter and crude fiber while highest fruitcarbohydrate contents was attained at 15 day irrigation interval over the 5-day interval. Delaying irrigationsignificantly depressed fruit protein content. Rice-straw mulch + 90kgN ha-1 or polythene mulch in combinationwith 45kgN ha-1 had more carbohydrate in fruits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".